Screening tools for clinical high risk for psychosis
Bibliographic record
Abstract
AIM: The purpose of this article was to review existing screening instruments that could be used to identify individuals who may be at increased risk for psychosis and to determine the suitability of these instruments. METHODS: Medline (Ovid) and PubMed were searched for peer-reviewed articles published in English, which reported performance evaluation of screening instruments for symptoms of high risk for psychosis. The articles' titles, abstracts and, when necessary, full texts were read to filter them against the selection criteria. Citations within relevant articles were hand searched for other potentially eligible studies. RESULTS: This selection strategy resulted in identifying 56 articles (including three articles available only in an abstract format) that reported performance evaluation of 17 screening instruments. CONCLUSIONS: The sensitivity of these scales ranged from 67% to 100% and the specificity ranged from 39% to 100%. The positive predictive value was less precise with scores ranging from 24% to 100%, and the negative predictive value ranging from 58% to 100%. There were several scales that might be useful for screening for individuals who are at increased risk for developing psychosis; however, the majority of measures are underexplored with poor validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".